Changes in the Contents of Carotenoids and Cis/Trans β-Carotenes of Fresh and Cooked Spinach in Foodservice Operations

Lim, Yaung-Iee

doi:10.3746/jkfn.2007.36.1.117

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…The cooking of frozen Brussels sprouts in water for 15 min resulted in 28% and 2% increases in cis-b-carotene and lutein contents respectively and 7% decrease of b-carotene level (Podsedek 2007). GliszczynskaSwiglo et al (2006) found an increase on both b-carotene and lutein content in steam-cooking and boiling of broccoli as compared with fresh broccoli but Lim (2007) observed in spinach that cooking did not alter the carotenoid profiles of the samples, only the level of all carotenoids that was greater in cooked samples than in fresh samples. Steaming increased the total carotenoids contents, especially that of trans-b-carotene.…”

Section: Cooking Methodologiesmentioning

confidence: 95%

Effect of domestic processing on bioactive compounds

et al. 2007

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Section: Cooking Methodologiesmentioning

confidence: 95%

Effect of domestic processing on bioactive compounds

et al. 2007

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“…Spinach is a highly perishable product with multiple nutrients such as minerals (Bhattacharjee et al, ), vitamins (Bunea et al, ; Tang, Qin, Dolnikowski, Russell, & Grusak, ; Tressler, Mack, & King, ), carotenes (Bunea et al, ; Lim, ), and antioxidant phenolic compounds (Pandjaitan, Howard, Morelock, & Gil, ). Spinach is widely consumed and forms a major part of human diet due to its nutritional properties.…”

Section: Introductionmentioning

confidence: 99%

Rapid and nondestructive detection of freshness quality of postharvest spinaches based on machine vision and electronic nose

Huang

et al. 2019

Journal of Food Safety

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Artificial sensory evaluation and physicochemical analysis tend to be tedious and time consuming. Therefore, this study employs machine vision, electronic nose (E‐nose), and multisensory data fusion to rapidly and nondestructively detect spinach freshness during storage. Spinach freshness during refrigeration was classified into four grades by 10 trained panelists. Machine vision and E‐nose were then used to obtain the image and odor information of the samples, respectively. K‐nearest neighbor (KNN), support vector machine (SVM), and back‐propagation artificial neural network (BPNN) were applied to predict spinach freshness. Result from the BPNN model based on machine vision achieved the same result as the KNN model with a classification accuracy of 85.42%. The BPNN model based on E‐nose achieved a better result than the SVM model with classification accuracies of 81.25% and 75.00%, respectively. Furthermore, the BPNN model based on multisensory data fusion greatly improved the detection accuracy of spinach freshness with a classification accuracy of 93.75%. Multisensory data fusion approaches based on machine vision and E‐nose are capable of intelligently detecting the freshness of postharvest spinach during storage. The main advantage is to allow a rapid and nondestructive detection of spinach freshness without using any chemical pretreatments. Practical Applications Fresh spinach is an important source of minerals, vitamins, carotenes, and other nutrients. Timely and accurately knowing the grade of spinach freshness before decay is of great interest in ensuring edible quality and reducing economic losses. Traditional analytical methods of spinach freshness tend to be tedious and time consuming. Novel analytical techniques such as machine vision and electronic nose (E‐nose) showed good feasibility in detecting spinach freshness during refrigerated storage. The detection accuracy could be highly improved based on the multisensory data fusion of machine vision and E‐nose. The results of this study confirmed that machine vision and E‐nose could constitute a rapid and nondestructive method to detect spinach freshness during storage instead of tedious and time‐consuming methods. The method of combining machine vision and E‐nose could be applied in the fruit and vegetable industry in the future.

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